As you set up your Data@UNIMI account and begin creating one or more dataverse(s) and dataset(s) inside your Data@UNIMI workspace, remember that well-structured and well-described dataverses and datasets are key for a FAIR use of research data, as this allows others to more easily access and understand your research data. Below are some key points to keep in mind and resources to consult as you get started on Data@UNIMi:
- Read carefully the Checklist Prepare your dataset, which can guide you each time that you create a dataverse/dataset.
- If you are uploading research data associated to a scientific publication, your dataset should be named Replication data for: “Title of the publication”. We also recommend to fill the metadata “Related publication” of your dataset(s) with the full citation of your scientific publication and its DOI.If you need to give your research data a more complex structure you can upload a zip file in your dataset with sub-folders, or, alternatively, you can create inside your dataverse a new dataverse with many datasets – with naming uniformity – inside it. We recommend reading carefully the guideline on How to share data related to a publication. Significantly, if the data you are uploading are related to a scientific publication still under review or submission, you can keep your dataset in draft form and unpublished on Data@UNIMI and allow reviewers to access the dataset by using the “private URL” option, as indicated in the guideline above. As soon as the related scientific publication is published, follow the instructions to the previous point and those in the guidelines to publish and share your dataset (and hence to make it public and open to everyone).
- If you are uploading data related to a published pre-print, follow the instructions set out above in the previous point. If, afterwards, the pre-print is submitted to a scientific journal, you can add the citation information of the published article, and link it to your pre-print, in the ‘related publication’ field of the dataset. If during the review of the article you are asked to apply significant changes to your data, consider creating a new dataset with the reviewed data and linking it to the dataset of the pre-print (with the old, unreviewed data). For further details, follow the guidelines on How to share data related to a publication.
- If you are uploading data produced in the framework of a research project, be sure that you are following the same data organization and data publication of your project’s DMP. Raw data, working data, deliverables and text documents should be preserved and shared with the members of the project via Drive or other departmental servers. If the project’s development leads to one or more publishable and publicly sharable dataset(s), you can use Data@UNIMI: be sure to consistently organize and structure your data by following the guideline on How to structure a dataverse for your project.
- Finally, if you are planning to upload raw data, think about it carefully. Remember that repositories like Data@UNIMI are devoted to publishing and sharing data that have been processed and/or represent the final stages of a research process and are thus ready for interoperability and reuse. Preferably, raw data should be preserved and shared with your research team via other platforms, as indicated above: make sure that you select relevant data and keep uncomplete data elsewhere. If you consider some of your work data relevant enough to be upload to Data@UNIMI, make sure that your dataset is named Work data for “name of the experiment”/”title of the publication”. For work data and processed data, it is key to describe the data exhaustively and document fully its provenance, along with all the processes, softwares, codes, and so on, used to obtain it, by filling as many fields of metadata as possible. Remember that Data@UNIMI is a FAIR repository, and data should be FAIR!